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Existing models miss critical visual evidence in metaphor understanding, but M$^3$R-Reasoner closes the gap, outperforming larger models in both accuracy and justification metrics.
Current LLMs struggle with cross-scenario summarization, revealing critical gaps in their reasoning capabilities and adaptability.
A principled framework for General World Models reveals the limitations of current systems and the architectural requirements for future progress.